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Day: November 8, 2022

Using Shiny on Python

David Saipe crosses the streams:

As someone who has zero experience using Shiny in R, the recent announcement that the framework had been made available to Python users inspired an opportunity for me to learn a new concept from a different perspective to most of my colleagues. I have been tasked with writing a Python related blog post, and having spent the past few weeks carrying out an analysis of Jumping Rivers’ Twitter data (@jumping_uk), creating a dashboard to display some of my findings and then writing about it seemed like a nice way to cap off my 6-week summer placement at Jumping Rivers.

This post will take you through some of the source code for the dashboard I created, whilst I provide a bit of context for the Twitter project itself. For a more bare-bones tutorial on using Shiny for Python, you can check out another recent Jumping Rivers blog post here. I suggest reading this first.

Read on to see how you can get started with Shiny on Python and what David thinks about the experience.

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Securing a Kafka Cluster

Dan Weston aims to secure an Apache Kafka cluster:

As part of our educational resources, Confluent Developer now offers a course designed to help you apply Confluent Cloud’s security features to meet the privacy and security needs of your organization. This blog post explores the need to implement security for your Apache Kafka® cluster, then briefly reviews the security features and advantages of using Confluent Cloud.

Click through for an overview. The course itself is free, as well.

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Deleting Data from SQL Server

Greg Larsen fills us in on an important command:

Over time data in SQL Server tables needs to be modified. There are two major different aspects of modifying data: updating and deleting. In my last article “Updating SQL Server Data” I discussed using the UPDATE statement to change data in existing rows of a SQL Server table. In this article I will be demonstrating how to use the DELETE statement to remove rows from a SQL Server Table.

This stays pretty simple but provides an effective overview of how to keep those tables tidy.

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Production-Grade Code

Rob Farley has seen some things:

I’m a consultant, so I see quite a lot of organisation’s “production code”. The quality they have in common is that it’s code that’s in production. That’s pretty much it.

I know in the days when I used to code JavaScript occasionally, there was the concept of “minifying” the code, which made a tighter version of it all, so that it would load better. This was code that was good for production. Hopefully most of the tests were happening on the minified code, because if that’s what was going into production, then you’d want to be sure that it worked, even if the pre-minification code worked fine. If you deployed code that wasn’t minified, then that wasn’t right.

But SQL doesn’t really have that concept.

Rob hits on the fundamental subjectivity of the concept and it’s worth a read.

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Thoughts on Page Life Expectancy

Denny Cherry shares a few thoughts on Page Life Expectancy:

One of the very misunderstood values that you can monitor in Microsoft SQL Server is Page Life Expectancy (PLE). I’ve read online that many people think that the value of Page Life Expectancy is worthless to monitor. And that really isn’t true. While Page Life Expectancy won’t tell you a specific problem by itself, it can be used to point you in the correct direction so that you know where to look.

There was a backlash against PLE, specifically the “PLE should be at least 300” concept, but Denny throws that away and digs into the actual benefit from observing this metric.

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Power BI Model Documenter Version 2.1.0

Marc Lelijveld has a new version of the Power BI Model Documenter:

It is long overdue, but time for a new updated version for the Power BI Model Documenter external tool! The latest release lasts from the first week of 2022 and since then close to 6000 people have downloaded the Model Documenter installer directly from the website, excluding the downloads and clones from the GitHub repository. I can’t share enough how happy I am with all the interactions I’ve had with people from all over the world providing feedback, asking questions and willing to contribute to the Model Documenter.

Read on to see what’s changed since the last release.

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DirectQuery Partitions on Hybrid Tables

Shabnam Watson noticed something interesting:

While I was tracing some Power BI datasets with Hybrid tables, I noticed that that the DirectQuery partition of a Hybrid table is used in all queries submitted to the Hybrid table, whether or not they are asking for data from that Direct Query partition. On first look, this does not look good because Power BI should be able to use partition pruning for a partitioned table, meaning it should only scan certain partitions. For example, if you ask for historical data from 5 years ago, the current day’s data is not needed to answer that query and hence, the Direct Query partition for today should not be used in the query.

But do read on for more information about what’s going on, as it seems there are layers of nuance here.

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